Abstract
To succeed in a market economy an economic entity should attract investments. The quality of accounting and analytical information about a company’s activities is an important factor in the decisions potential investors make. Accounting (fnancial) reporting is a signifcant part of the informational support of the company’s activity. The problem of reliability of the accounting (fnancial) statements has always been relevant. According to the modern concept of business audit, audit is primarily understood as an activity aimed at reducing business risks. Nowadays the main task of an auditor is to provide assurance that the accounting (fnancial) reporting does not contain signifcant misstatements because of its falsifcation, or mistakes made by employees of the auditee. Assessing the risk of fnancial statements falsifcation is an urgent and diffcult task. Though the term “falsifcation of fnancial statements” is widely used and seems clear in terms of common sense, the scientifc understanding and normative defnition of this concept is not so defnite. This article analyzes the concept of “falsifcation of fnancial statements” and approaches to its defnition in foreign and domestic practice; reviews modern tools to identify risks of fnancial statements falsifcation; discusses issues related to the use of mathematical models to identify the risk of fnancial statements falsifcation. To do this the authors analyze the index model of the American scientist M. Benish, carry out econometric tests within the assumptions of the Gauss-Markov theorem and propose a variant of developing an index model to detect accounting (fnancial) statements falsifcation. An attempt was made to create a model to identify the risk of reporting information falsifcation with a certain degree of probability that could be applied in Russia. To create such a model, called NARM, there were selected 75 reports of Russian organizations, of which 1/3 were falsifed. This model makes it possible to identify the probability of fnancial statements falsifcation to within 76%.
Highlights
A B S TR A CT To succeed in a market economy an economic entity should attract investments
Nowadays the main task of an auditor is to provide assurance that the accounting reporting does not contain significant misstatements because of its falsification, or mistakes made by employees of the auditee
This article analyzes the concept of “falsification of financial statements” and approaches to its definition in foreign and domestic practice; reviews modern tools to identify risks of financial statements falsification; discusses issues related to the use of mathematical models to identify the risk of financial statements falsification
Summary
NARM1a, был получен процент точности модели, который составил 76%. Далее были проверены предпосылки теоремы Гаусса–Маркова 11 о гомоскедастичности остатков Var (u1) = Var (u2) = ... = Var (un) = σ2 путем проведения теста Голдфелда–Квандта и некоррелированности остатков cov (ui , uj) = 0 при i ≠ j путем проведения теста Дарбина–Уотсона. = Var (un) = σ2 путем проведения теста Голдфелда–Квандта и некоррелированности остатков cov (ui , uj) = 0 при i ≠ j путем проведения теста Дарбина–Уотсона. Тест Голдфелда–Квандта на проверку предпосылки о гомоскедастичности остатков заключается в ранжировании величины zi по возрастанию, где величина zi представляет собой. При этом значение Fкрит находится по распределению Фишера: Fкрит (1 – α ; v1; v2), где v1 = v2 = n′′ – (k + 1). Тест Дарбина–Уотсона на проверку предпосылки о некоррелированности остатков заключается в нахождении статистики Дарбина–Уотсона (DW). В модели NARM1a рассчитанные значения составили соответственно: 0,552≤ 2,272 1,812 ≤ 2,272. Затем по n′′ упорядоченным первым и последним уравнениям из выборки n вычисляются МНКоценки параметров модели и величины. Статистика Дарбина–Уотсона в модели NARM1a составляет DW = 1,944, критические значения dl и du равны 1,404 и 1,805 соответственно
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